Showing papers by "Conchita D'Ambrosio published in 2022"

Characteristics associated with COVID-19 vaccine hesitancy

Abstract: Understanding what lies behind actual COVID-19 vaccine hesitancy is fundamental to help policy makers increase vaccination rates and reach herd immunity. We use June 2021 data from the COME-HERE survey to explore the predictors of actual vaccine hesitancy in France, Germany, Italy, Luxembourg, Spain and Sweden. We estimate a linear-probability model with a rich set of covariates and address issues of common-method variance. 13% of our sample say they do not plan to be vaccinated. Post-Secondary education, home-ownership, having an underlying health condition, and one standard-deviation higher age or income are all associated with lower vaccine hesitancy of 2-4.5% points. Conservative-leaning political attitudes and a one standard-deviation lower degree of confidence in the government increase this probability by 3 and 6% points respectively. Vaccine hesitancy in Spain and Sweden is significantly lower than in the other countries.

Gender, loneliness and happiness during COVID-19

Abstract: We analyse a measure of loneliness from a representative sample of German individuals interviewed in both 2017 and at the beginning of the COVID-19 pandemic in 2020. Both men and women felt lonelier during the COVID-19 pandemic than they did in 2017. The pandemic more than doubled the gender loneliness gap: women were lonelier than men in 2017, and the 2017-2020 rise in loneliness was far larger for women. This rise is mirrored in life-satisfaction scores. Men's life satisfaction changed only little between 2017 and 2020; yet that of women fell dramatically, and sufficiently so to produce a female penalty in life satisfaction. We estimate that almost all of this female penalty is explained by the disproportionate rise in loneliness for women during the COVID-19 pandemic.

The impact of COVID-19 lockdown stringency on loneliness in five European countries

Abstract: The coronavirus pandemic has forced governments to implement a variety of different dynamic lockdown-stringency strategies in the last two years. Extensive lockdown periods could have potential unintended consequences on mental health, at least for at-risk groups.We present novel evidence on the heterogeneous direct and indirect effects of lockdown-stringency measures on individuals' perception of social isolation (i.e. loneliness) using panel data from five European countries (Germany, France, Spain, Italy and Sweden), which tracks changes in both in-person and remote social interactions between May 2020 and March 2021.We combine data from the COME-HERE panel survey (University of Luxembourg) and the Oxford COVID-19 Government Response Tracker (OxCGRT). We implement a dynamic mixture model in order to estimate the loneliness sub-population classes based on the severity of loneliness, as well as the evolution of social interactions.While loneliness is remarkably persistent over time, we find substantial heterogeneity across individuals, identifying four latent groups by loneliness severity. Group membership probability varies with age, gender, education and cohabitation status. Moreover, we note significant differences in the impact of social interactions on loneliness by degree of severity. Older people are less likely to feel lonely, but were more affected by lockdown measures, partly due to a reduction in face-to-face interactions. On the contrary, the younger, especially those living alone, report high levels of loneliness that are largely unaffected by changes in the pandemic after lockdown measures were initially implemented.Understanding the heterogeneity in loneliness is key for the identification of at-risk populations that can be severely affected by extended lockdown measures. As part of public-health crisis-response systems, it is critical to develop support measures for older individuals living alone, as well as promoting continuous remote communication for individuals more likely to experience high levels of loneliness.

Economic insecurity and political preferences

Abstract: Economic insecurity has attracted growing attention, but there is no consensus as to its definition. We characterize a class of individual economic-insecurity measures based on the time profile of economic resources. We apply this economic-insecurity measure to political-preference data in the USA, UK, and Germany. Conditional on current economic resources, economic insecurity is associated with both greater political participation (support for a party or the intention to vote) and more support for conservative parties. In particular, economic insecurity predicts greater support for both Donald Trump before the 2016 US Presidential election and the UK leaving the European Union in the 2016 Brexit referendum.

Measuring COVID-19 Vaccine Hesitancy: Consistency of Social Media with Surveys

Abstract: We validate whether social media data can be used to complement social surveys to monitor the public’s COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals’ vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic.

Pace of aging, family environment and cognitive skills in children and adolescents

Abstract: Pace of aging is an epigenetic clock which captures the speed at which someone is biologically aging compared to the chronological-age peers. We here use data from the Avon Longitudinal Study of Parents and Children (ALSPAC) to investigate the interrelation between the study children's parental social class at birth, and their pace of aging and cognitive skills measures in childhood and adolescence. We show that children from lower parental social classes display faster pace of aging and that the social class gradient in pace of aging is strongest in adolescence. About one third of this association can be explained by other socio-economic and demographic covariates, as well as life events. Similarly, study children's pace of aging manifests a negative association with their measures of cognitive skills in late adolescence only. This association becomes stronger as the contemporary pace of aging of the mother becomes faster. Our results seem to identify adolescence as the period of life when pace of aging, family environment and cognitive skills measures begin to interact.

Correction to: the Fall in Income Inequality during COVID-19 in Four European Countries

Abstract: [This corrects the article DOI: 10.1007/s10888-021-09499-2.].

Correction to: the Fall in Income Inequality during COVID-19 in Four European Countries

Abstract: [This corrects the article DOI: 10.1007/s10888-021-09499-2.].

Human Wellbeing and Machine Learning

Abstract: There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms – i.e. material conditions, health, and meaningful social relations – are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches. ∗These authors are joint first authors. The displayed order of these authors is random and was determined by the AEA randomization tool, confirmation code oJsh ZMZJwhH. Author affiliations: Ekaterina Oparina: London School of Economics; Niccolò Gentile, Conchita D’Ambrosio and Alexandre Tkatchenko: University of Luxembourg; Caspar Kaiser and Jan-Emmanuel De Neve: University of Oxford; Andrew E. Clark: Paris School of Economics CNRS. We thank Filippo Volpin for excellent research assistance and Sid Bhushan for early discussions on the topic. We are grateful to the participants of the LSE Wellbeing Seminar for their comments and suggestions. Funding via the ERC Grant Agreement n. 856455, and the Institute for Advanced Studies, University of Luxembourg, Grant DSEWELL is gratefully acknowledged. We thank The Gallup Organization for providing access to their data for this research project. 1 ar X iv :2 20 6. 00 57 4v 1 [ ec on .E M ] 1 J un 2 02 2

The Distributive Impact of the Luxembourg Tax-Benefit System: A More Comprehensive Measurement

Abstract: This article studies the distribution of income in Luxembourg by integrating two aspects that have been previously neglected: indirect taxation and in-kind transfers. The integration of the latter is essential in Luxembourg, the country with the highest public expenditure per capita in the Organisation for Economic Co-operation and Development (OECD). These issues have been understudied because of some methodological challenges, including the lack of data containing all the necessary information for this type of analysis. However, with the EUROMOD microsimulation model, different data sources, and imputation methods, we are able to obtain a more complete view of the income distribution in Luxembourg. We find that, as in many developed countries, indirect taxes are regressive. On the other hand, in-kind transfers play an important role in reducing income inequality, in particular through education and health services.